Abstract
Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method of developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user.
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References
AbdulRahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476–5497 (2021)
Aledhari, M., Razzak, R., Parizi, R.M., Saeed, F.: Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699–140725 (2020)
Alegre, U., Augusto, J.C., Clark, T.: Engineering context-aware systems and applications: a survey. J. Syst. Softw. 117, 55–83 (2016)
Augusto, J., Kramer, D., Alegre, U., Covaci, A., Santokhee, A.: The user-centred intelligent environments development process as a guide to co-create smart technology for people with special needs. Univ. Access Inf. Soc. 17(1), 115–130 (2018)
Augusto, J.C., Quinde, M., Oguego, C., Manuel, J.G.: Context-aware systems architecture (CaSa). Cybern. Syst. Anal. (2020)
Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sc. 34, 450–457 (2014)
Forkan, A.R.M., Hu, W.: A context-aware, predictive and protective approach for wellness monitoring of cardiac patients. In: 2016 Computing in Cardiology Conference (CinC), pp. 369–372. IEEE (2016)
Jakkula, V.: Tutorial on support vector machine (SVM). School of EECS, Washington State University, 37 (2006)
Kunnath, A.T., Nadarajan, D., Mohan, M., Ramesh, M.V.: Wicard: a context aware wearable wireless sensor for cardiac monitoring. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1097–1102. IEEE (2013)
Li, J.P., Berry, D., Hayes, R.: A mobile ECG monitoring system with context collection. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds.) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol. 22, pp. 1222–1225. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-89208-3_292
Li, Z., Sharma, V., Mohanty, S.P.: Preserving data privacy via federated learning: challenges and solutions. IEEE Consum. Electron. Mag. 9(3), 8–16 (2020)
McMahan, H.B., Moore, E., Ramage, D., Arcas, B.A.: Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629 (2016)
Miao, F., Cheng, Y., He, Y., He, Q., Li, Y.: A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the smartphone. Sensors 15(5), 11465–11484 (2015)
Mittal, S., Movsowitz, C., Steinberg, J.S.: Ambulatory external electrocardiographic monitoring: focus on atrial fibrillation. J. Am. Coll. Cardiol. 58(17), 1741–1749 (2011)
Ogbuabor, G.O., Augusto, J.C., Moseley, R.: Physical Activity Recognition Dataset (2021). https://mdx.figshare.com/articles/dataset/Physical_Activity_Recognition_Dataset/14798667
Ogbuabor, G.O., Augusto, J.C., Moseley, R., van Wyk, A.: Context-aware approach for cardiac rehabilitation monitoring. In: Intelligent Environments 2020: Workshop Proceedings of the 16th International Conference on Intelligent Environments, vol. 28, p. 167. IOS Press (2020)
Ogbuabor, G.O., Augusto, J.C., Moseley, R., van Wyk, A.: Context-aware system for cardiac condition monitoring and management: a survey. Behav. Inf. Technol. 1–18 (2020)
Sannino, G., De Pietro, G.: A smart context-aware mobile monitoring system for heart patients. In: 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), pp. 655–695. IEEE (2011)
Särelä, A., Korhonen, I., Salminen, J., Koskinen, E., Kirkeby, O., Walters, D.: A home-based care model for outpatient cardiac rehabilitation based on mobile technologies. In: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare, pp. 1–8. IEEE (2009)
Shanmathi, N., Jagannath, M.: Computerised decision support system for remote health monitoring: a systematic review. IRBM 39(5), 359–367 (2018)
Varshney, U.: Pervasive Healthcare Computing: EMR/EHR, Wireless and Health Monitoring. Springer, Heidelberg (2009). https://doi.org/10.1007/978-1-4419-0215-3
WHO: Cardiovascular diseases (CVDs) (2017). https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
Yu, W., Liu, T., Valdez, R., Gwinn, M., Khoury, M.J.: Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes. BMC Med. Inform. Decis. Mak. 10(1), 1–7 (2010)
Yürür, Ö., Liu, C.H., Sheng, Z., Leung, V.C., Moreno, W., Leung, K.K.: Context-awareness for mobile sensing: a survey and future directions. IEEE Commun. Surv. Tutor. 18(1), 68–93 (2014)
Zhang, W., Thurow, K., Stoll, R.: A context-aware mhealth system for online physiological monitoring in remote healthcare. Int. J. Comput. Commun. Control 11(1), 142–156 (2015)
Zimetbaum, P., Goldman, A.: Ambulatory arrhythmia monitoring: choosing the right device. Circulation 122(16), 1629–1636 (2010)
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Ogbuabor, G.O., Augusto, J.C., Moseley, R., van Wyk, A. (2021). Context-Aware Support for Cardiac Health Monitoring Using Federated Machine Learning. In: Bramer, M., Ellis, R. (eds) Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science(), vol 13101. Springer, Cham. https://doi.org/10.1007/978-3-030-91100-3_22
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